ragflow/docs/guides/agent/general_purpose_chatbot.md

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sidebar_position: 2
slug: /general_purpose_chatbot
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# Create chatbot

Create a general-purpose chatbot.

---

:::danger DEPRECATED!
A new version is coming soon.
:::

Chatbot is one of the most common AI scenarios. However, effectively understanding user queries and responding appropriately remains a challenge. RAGFlow's general-purpose chatbot agent is our attempt to tackle this longstanding issue.  

This chatbot closely resembles the chatbot introduced in [Start an AI chat](../chat/start_chat.md), but with a key difference - it introduces a reflective mechanism that allows it to improve the retrieval from the target knowledge bases by rewriting the user's query.

This document provides guides on creating such a chatbot using our chatbot template.

## Prerequisites

1. Ensure you have properly set the LLM to use. See the guides on [Configure your API key](../models/llm_api_key_setup.md) or [Deploy a local LLM](../models/deploy_local_llm.mdx) for more information.
2. Ensure you have a knowledge base configured and the corresponding files properly parsed. See the guide on [Configure a knowledge base](../dataset/configure_knowledge_base.md) for more information.
3. Make sure you have read the [Introduction to Agentic RAG](./agent_introduction.md).

## Create a chatbot agent from template

To create a general-purpose chatbot agent using our template:

1. Click the **Agent** tab in the middle top of the page to show the **Agent** page.
2. Click **+ Create agent** on the top right of the page to show the **agent template** page.
3. On the **agent template** page, hover over the card on **General-purpose chatbot** and click **Use this template**.  
   *You are now directed to the **no-code workflow editor** page.*

   ![workflow_editor](https://github.com/user-attachments/assets/52e7dc62-4bf5-4fbb-ab73-4a6e252065f0)

:::tip NOTE
RAGFlow's no-code editor spares you the trouble of coding, making agent development effortless.
:::

## Understand each component in the template

Here’s a breakdown of each component and its role and requirements in the chatbot template:

- **Begin**
  - Function: Sets an opening greeting for users.
  - Purpose: Establishes a welcoming atmosphere and prepares the user for interaction.

- **Interact**
  - Function: Serves as the interface between human and the bot.
  - Role: Acts as the downstream component of **Begin**.  

- **Retrieval**
  - Function: Retrieves information from specified knowledge base(s).
  - Requirement: Must have `knowledgebases` set up to function.

- **Relevant**
  - Function: Assesses the relevance of the retrieved information from the **Retrieval** component to the user query.
  - Process:  
    - If relevant, it directs the data to the **Generate** component for final response generation.
    - Otherwise, it triggers the **Rewrite** component to refine the user query and redo the retrival process.

- **Generate**
  - Function: Prompts the LLM to generate responses based on the retrieved information.  
  - Note: The prompt settings allow you to control the way in which the LLM generates responses. Be sure to review the prompts and make necessary changes.

- **Rewrite**:  
  - Function: Refines a user query when no relevant information from the knowledge base is retrieved.  
  - Usage: Often used in conjunction with **Relevant** and **Retrieval** to create a reflective/feedback loop.  

## Configure your chatbot agent

1. Click **Begin** to set an opening greeting:  
   ![opener](https://github.com/user-attachments/assets/4416bc16-2a84-4f24-a19b-6dc8b1de0908)

2. Click **Retrieval** to select the right knowledge base(s) and make any necessary adjustments:  
   ![setting_knowledge_bases](https://github.com/user-attachments/assets/5f694820-5651-45bc-afd6-cf580ca0228d)

3. Click **Generate** to configure the LLM's summarization behavior:  
   3.1. Confirm the model.  
   3.2. Review the prompt settings. If there are variables, ensure they match the correct component IDs:  
   ![prompt_settings](https://github.com/user-attachments/assets/19e94ea7-7f62-4b73-b526-32fcfa62f1e9)

4. Click **Relevant** to review or change its settings:  
   *You may retain the current settings, but feel free to experiment with changes to understand how the agent operates.*
   ![relevant_settings](https://github.com/user-attachments/assets/9ff7fdd8-7a69-4ee2-bfba-c7fb8029150f)

5. Click **Rewrite** to select a different model for query rewriting or update the maximum loop times for query rewriting:  
   ![choose_model](https://github.com/user-attachments/assets/2bac1d6c-c4f1-42ac-997b-102858c3f550)
   ![loop_time](https://github.com/user-attachments/assets/09a4ce34-7aac-496f-aa59-d8aa33bf0b1f)

:::danger NOTE
Increasing the maximum loop times may significantly extend the time required to receive the final response.
:::

1. Update your workflow where you see necessary.

2. Click to **Save** to apply your changes.  
   *Your agent appears as one of the agent cards on the **Agent** page.*

## Test your chatbot agent

1. Find your chatbot agent on the **Agent** page:  
   ![find_chatbot](https://github.com/user-attachments/assets/6e6382c6-9a86-4190-9fdd-e363b7f64ba9)

2. Experiment with your questions to verify if this chatbot functions as intended:  
   ![test_chatbot](https://github.com/user-attachments/assets/c074d3bd-4c39-4b05-a68b-1fd361f256b3)